Zero-Shot Gaze-based Volumetric Medical Image Segmentation

TL;DR

This paper explores using eye gaze as a new input modality for interactive 3D medical image segmentation, demonstrating its efficiency and potential to complement existing methods like SAM-2 and MedSAM-2.

Contribution

It introduces eye gaze as a novel prompt modality for volumetric medical image segmentation and evaluates its effectiveness with existing models.

Findings

Gaze-based prompts are more time-efficient than bounding boxes.

Gaze prompts achieve slightly lower segmentation quality but are promising.

Gaze can serve as a complementary input modality for segmentation.

Abstract

Accurate segmentation of anatomical structures in volumetric medical images is crucial for clinical applications, including disease monitoring and cancer treatment planning. Contemporary interactive segmentation models, such as Segment Anything Model 2 (SAM-2) and its medical variant (MedSAM-2), rely on manually provided prompts like bounding boxes and mouse clicks. In this study, we introduce eye gaze as a novel informational modality for interactive segmentation, marking the application of eye-tracking for 3D medical image segmentation. We evaluate the performance of using gaze-based prompts with SAM-2 and MedSAM-2 using both synthetic and real gaze data. Compared to bounding boxes, gaze-based prompts offer a time-efficient interaction approach with slightly lower segmentation quality. Our findings highlight the potential of using gaze as a complementary input modality for interactive 3D medical image segmentation.